Event-Human3.6m dataset.
In the previous section we highlight some limitations of the DHP19 dataset [1]}, especially related to the narrowness of movements and activities that it provides. To solve these gaps, we contribute with a new simulated datasets based on the Human3.6m dataset [2]}, [3]}. Human3.6 recordings include 11 subjects and different activities from real scenarios, such as walking with a dog, talking at the phone, and giving directions. Consequently, extensive research has adopted the standard Human3.6m dataset to evaluate monocular Human Pose Estimation methods [4]}, [5]}, [6]}. We believe event-based research will benefit from our Event-Human3.6m, as it extends DHP19 with more challenging scenarios and provides a new benchmark for monocular human pose estimation algorithms.
We adopt the ESIM-Py simulator [7]} to convert the RGB recordings of Human3.6m into events and synchronize raw joints ground-truth with events frames through interpolation (Figure REF ). As a result, Event-Human3.6m and DHP19 have comparable ground-truths and event frames. In the following sections we reports extensive experiments on both DHP19 and Event-Human3.6m to test the benefits of our proposal.
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